Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm

ECG beat classification using particle swarm optimization and radial basis function neural network

Convolutional Neural Networks (CNNs) have become a dominant solution for Electrocardiogram (ECG) beat classification, owing to their superior feature extraction capabilities. However, traditional CNN-based accelerators often encounter limitations such as large parameter counts, limited configurability, and inefficient hardware utilization. In this work, we propose a lightweight one-dimensional CNN (1D-CNN) inspired by the InceptionNet architecture, specifically designed for efficient and accurate ECG beat classification. The model integrates customized Inception blocks with parallel convolutional layers of varying kernel sizes to capture multi-scale temporal features. To reduce model complexity and enhance efficiency, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1 \times 1$</tex> convolutions are used for dimensionality reduction, resulting in a compact network with only 7,740 parameters. Additionally, we present a hardware accelerator that exploits spatial data reuse across multiple processing units and employs dual-port RAM for concurrent data access, improving throughput and memory efficiency. When implemented on hardware with parallel and pipelined execution at 100 MHz, the system achieves a classification accuracy of 99.32%, sensitivity of 98.50%, specificity of 99.58%, and a positive predictive value (PPV) of 98.30% across five ECG classes with the MIT-BIH database, demonstrating its suitability for real-time deployment in resource-constrained environments.

Least Square Support Vector Machine (LS-SVM) is a very powerful tool for pattern recognition and function estimation. In this paper, LS-SVM has been used to construct software sensors in an application to a fed-batch yeast fermentation process. Comparisons have been made between results from LS-SVM and software sensors using Multiway Partial Least Squares (MPLS) and Extended Kalman Filters (EKF). The LS-SVM algorithm is introduced firstly and then applied to a yeast fed-batch fermentation process to provide soft-sensing facilities. The soft-sensing capabilities of the LS-SVM approach are found to compare favorably with the results using EKF and MPLS.

This work presents proposing two artificial intelligence methods including Least squares support vector machine (LSSVM) and Adaptive neuro fuzzy inference system (ANFIS) for the prediction of caustic current efficiency (CCE) and cell voltage as a function of pH, current density, brine concentration, electrolyte velocity, operating temperature, and run time. The predictions of LSSVM and ANFIS models were evaluated by the experimental values of this process graphically and statistically. The overall R-squared values of LSSVM and ANFIS for prediction of CCE were 0.999 and 0.972, respectively. On the other hand, these values for cell voltage prediction were 1 and 0.998. According to the CCE and cell voltage predictions results, LSSVM algorithm has great performance in prediction of chlor-alkali membrane cell processes. Furthermore, artificial intelligence methods can have wide use in electrolytic processes to enhance power consumption.

This study presents a novel approach for the modelling of transformer core magnetisation characteristics under DC bias condition by using the inverse Jiles–Atherton (J–A) model. An improved shuffled frog leaping algorithm (SFLA) is proposed to identify the five parameters of J–A model, where an adaptive chaotic mutation operation is added in the global searching process to increase the identification accuracy. With the proposed algorithm, the J–A model parameters under different DC components are identified based on the DC‐bias experiment on the real transformer. The conventional SFLA and particle swarm optimisation (PSO) method are also applied to identify the parameters of J–A model. All the identified results are compared with the measured B–H curves to verify their identification accuracy. Moreover, the least square support vector machine (LSSVM) algorithm is used to predict the J–A model parameters of transformer under larger DC component from the previously identified parameters in smaller DC. The calculated results have shown that the improved SFLA has higher identification accuracy than the conventional SFLA and PSO methods. Furthermore, LSSVM algorithm can effectively forecast the transformer magnetisation character under large DC bias condition, which is beneficial for the research of transformer DC bias problem.

Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation

The prediction of wastes generated in the hospital will help their management for several activities like storage, transport and disposing. This chapter adopts Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM) and Genetic Programming (GP) in order to estimate the rate of medical waste generation. In the event of predicting the rate, type of hospital, capacity and bed occupancy has been used as inputs of SVM, LSSVM and GP. SVM is based on statistical learning theory, which provides an elegant tool for nonlinear system modeling. LSSVM is the re-formulation to the general SVM. GP, a best part of evolutionary algorithm and also the specification of Genetic Algorithm (GA). These SVM, LSSVM and GP have been used as the regression techniques. The results show the performance of the developed SVM, LSSVM and GP models were elegant and outstanding.

The objective of this paper was to determine soluble solids content (SSC) of intact Gannan navel orange by a portable near-infrared (NIR) spectrometer with the optical fiber in the wavelength range of 551~950nm. The effective wavelength regions (EWs) were chosen from the spectra pro-processed by second derivative by interval partial least square (iPLS) and backward interval partial least square (Bipls). Then the partial least square (PLS) and least square support vector machine (LS-SVM) models were developed with EWs. 60 unknown samples were used to evaluate the performance of them. The LS-SVM model was better than others with EWs chosen by Bilps. The correlation coefficient (R) and root mean square error of prediction (RMSEP) for LS-SVM (Bipls) were 0.86 and 0.55°Brix. The results showed that the portable NIR combination with LS-SVM was a feasible method to determine SSC of intact Gannan navel orange nondestructively.

The prediction of wastes generated in the hospital will help their management for several activities like storage, transport and disposing. This chapter adopts Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM) and Genetic Programming (GP) in order to estimate the rate of medical waste generation. In the event of predicting the rate, type of hospital, capacity and bed occupancy has been used as inputs of SVM, LSSVM and GP. SVM is based on statistical learning theory, which provides an elegant tool for nonlinear system modeling. LSSVM is the re-formulation to the general SVM. GP, a best part of evolutionary algorithm and also the specification of Genetic Algorithm (GA). These SVM, LSSVM and GP have been used as the regression techniques. The results show the performance of the developed SVM, LSSVM and GP models were elegant and outstanding.

The minimal complexity support vector machine is a fusion of the support vector machine (SVM) and the minimal complexity machine (MCM), and results in maximizing the minimum margin and minimizing the maximum margin. It works to improve the generalization ability of the L1 SVM (standard SVM) and LP (Linear Programming) SVM. In this paper, we discuss whether it also works for the LS (Least Squares) SVM. The minimal complexity LS SVM (MLS SVM) is trained by minimizing the sum of squared margin errors and minimizing the maximum margin. This results in solving a set of linear equations and a quadratic program, alternatingly. According to the computer experiments for two-class and multiclass problems, the MLS SVM does not outperform the LS SVM for the test data although it does for the cross-validation data.

This article adopts three soft computing techniques including support vector machine (SVM), least square support vector machine (LSSVM) and relevance vector machine (RVM) for prediction of status of epimetemorphic rock slope. The input variables of SVM, LSSVM and RVM are bulk density, height, inclination, cohesion and internal friction angle. There are 53 datasets which have been used to develop the SVM, LSSVM and RVM models. The developed SVM, LSSVM and RVM give equations for prediction of status of epimetemorphic rock slope. The performance of SVM, LSSVM and RVM is 100%. A comparative study has been presented between the developed SVM, LSSVM and RVM. The results confirm that the developed SVM, LSSVM and RVM are effective tools for prediction of status of epimetemorphic rock slope.

A new combination of particle-swarm optimization (PSO) and the least-squares support vector machine (LS-SVM) technique for FDTD time-series forecasting is presented. In this paper, the PSO is extended to optimize the hyperparameter used in the LS-SVM algorithm. The numerical simulations demonstrate that the PSO method can efficiently obtain the optimal value of the hyperparameter used in the LS-SVM algorithm. And the PSO_LS-SVM method can improve the computational efficiency of the FDTD algorithm, as compared to the direct FDTD method. © 2005 Wiley Periodicals, Inc. Microwave Opt Technol Lett 48: 141–144, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21288

Aimed at the non-stationary characteristics of rolling bearing vibration signal,a fault diagnosis method was proposed based on local-wave method and KPCA(kernel principal component analysis)-LSSVM(least squares support vector machine).Firstly,local wave decomposition was used to decompose rolling bearing vibration signal into several intrinsic mode function(IMF),whose feature energy,singular values and AR model parameters were computed as initial feature vectors.Secondly,ini tial feature vectors were mapped into a higher-dimensional space with KPCA,and the kemel principal components were extracted as new feature vectors,which used as the input of LSSVM for fault classification.The experimental results show the KPCA-LSSVM method improves LSSVM's classification performance by KPCA obtaining additional discriminative information,and has better generalization than direct LSSVM method,and can identify rolling bearing fault patterns more accurately.

An improved least squares support vector machines (LS-SVM) was proposed to improve the sparse and robust performance of LS-SVM in the small samples prediction. The sparse and robust performance could be improved through adding elements of weighted LS-SVM and robust LS-SVM. We introduced a contrast experiment for ATE parameters prediction control through the three methods of neural network, LS-SVM and improved LS-SVM algorithm. Simulation results show that the improved LS-SVM algorithm has good performance in ATE parameters prediction, which succeeds in stability assessment for an aviation ATE.

In this paper, the conventional mechanism model of laminar flow cooling of hot-rolled strips is modified and optimized. And the new prediction model can express the laminar cooling process unambiguously, in which, the least squares support vector machines(LS-SVM) learning machine is introduced to promote the accuracy of the temperature-varying parameters and the genetic algorithm is proposed to identify the system of the temperature-varying parameters. The model can be used to calculate the temperature along the length direction and the thickness direction at the same time. The movement of the steel plate and the change of the valve can be obtained.KeywordsLaminar coolingLS-SVMMachine learningGenetic algorithmPrediction model